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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Bassant Tarek Radwa Taha Ranpreet Kaur Shereen Afifi Graduate student at the Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Master Candidate at the Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Lecturer in the Department of Computer Engineering and Artificial Intelligence Media Design School Auckland New Zealand Lecturer at the Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt

Skin cancer is one of the deadliest types of cancers that need to be detected at an early stage as it can widely spread affecting other organs of the body. Thus, our objective is to develop an efficient mobile application dedicated to the early detection of skin cancer in primary healthcare at a low cost. In the design of an Android-based mobile application, EfficientNetV2 deep learning model achieved the highest accuracy and feasibility of deployment as an offline model compared to other CNN pre-trained models. The designed deep learning system classifies three classes: healthy, benign, and malignant skin lesions, and further classification of malignant lesions into Basal Cell Carcinoma, Melanoma, or Nevus (the most common skin cancer malignancies) is performed. The developed two-stage classification system has achieved the highest accuracy with 91% and 90% using the EfficientNetV2-model in the designed *** relevance: Skin cancer is a severe disease that requires early detection, so we are proposing an efficient mobile application that would help dramatically in increasing survival rates by providing a free tool for real-time detection and classification of suspicious mole captured images.

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm

Impact of Image Enhancement for Osteoporosis Detection Based...

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computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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Time efficiency and precision of splinted complete arch implant scanning

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Journal of Prosthetic Dentistry 2025年

作者： Abdelrehim, Aly bin Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate PhD student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya Kuala Lumpur Malaysia Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering College of Computer and Cyber Sciences University of Prince Mugrin Madinah Saudi Arabia

Statement of problem: The precision and accuracy of intraoral scanners for complete arch implant scanning is questionable as it is affected by the absence of anatomic features, the presence of large homogenous areas, and the lack of clear reference points, all of which create inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to test a newly designed scan-aid apparatus for evaluating the effect of geometric heterogeneity and additional reference points on the time efficiency and scanning precision of complete arch implant scanning. Material and methods: A total of 60 scans (20 per group) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants;2 anterior implants in parallel axial orientation;and 2 distal implants with a 30-degree posterior inclination. The scanning procedure was alternated between scanning with the scan-aid apparatus (test groups;4IP+, 4IA+, 2IP+) and without (control groups;4IP-, 4IA-, 2IP-). A 3-dimensional (3D) inspection software program was used to assess scan precision. Test group scans were compared with a reference scan for repeatability, with and without the scan-aid apparatus. One scan per group was the reference;the others were superimposed by using a best-fit algorithm. The 3D standard deviations were recorded for repeatability analysis. To assess time efficiency, the total scanning time, actual scanning time, and time lost were measured. A paired t test was used to compare means between the groups (α=.05). Results: Precision was significantly higher in the test groups using the scan-aid apparatus than in the control groups (P<.05). Furthermore, scanning with the scan-aid apparatus was significantly more time efficient (P<.05). Conclusions: Additional reference points and geometric heterogeneity using a scan-aid apparatus significantly improved the precision and time efficiency of co

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The impact of Instagram on young Adult's social comparison, colourism and mental health: Indian perspective

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International Journal of Information Management Data Insights 2022年第1期2卷

作者： Sharma, Aparna Sanghvi, Kavish Churi, Prathamesh Final year student Department of Mechatronics Engineering Mukesh Patel School of Technology Management & Engineering NMIMS University Mumbai India Graduate Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering NMIMS University Mumbai India Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering NMIMS University Mumbai India

This paper explores persuasive issues such as Social Comparison, Colourism, and Mental Health and their relationship with Instagram use, focusing on young adults living in India. Participants (N = 726) were recruited to fill up a questionnaire, and the self-reported data was used to test our hypothesis. The study followed a correlational, non-experimental approach to investigate the mediation models as proposed. Structural Equational Modelling eluded a positive and significant relationship between age and the social issues with control variables as the frequency and time spent on Instagram. Moreover, it was found that social comparison can lead to colourism and mental health issues. Gender was, however, found to have insignificant relation with three parameters. Qualitative analysis was supported to find important implications for all Instagram stakeholders. Discussions were drawn indicating the need to raise awareness regarding these issues to enjoy social media while safeguarding the user's well-being. The paper provides critical key findings delving into the realms of these parameters, and such implications are further discussed to understand its reflection in online spaces. © 2022

关键词： Colourism India Instagram Mental health Social comparison Social media

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Deep learning-based detection of incisal translucency patterns

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Journal of Prosthetic Dentistry 2025年

作者： Shetty, Sthithika Gali, Sivaranjani R, Venkatesh MS, Jeswin MN, Thippeswamy Postgraduate student Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Head Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Graduate student Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Ramaiah Institute of Technology Bangalore India Principal Dr. Ambedkar Institute of Technology Bangalore India

Statement of problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking. Purpose: The purpose of this study was to assess the accuracy of deep learning models in predicting the translucency patterns of anterior teeth. Material and methods: Approximately 240 Joint Photographic Experts Group (JPEG) images of anterior teeth from participants over 18 years were collected using a smartphone. These images were resized to 224×224 pixels and classified by the presence or absence of translucency. Augmentation techniques enhanced the training dataset, and a 3-model deep learning approach was used: YOLOv5 detected central incisors, Vision Transformers (ViT) identified translucency, and U-Net segmented the translucent areas. The images were split 80 to 20 for training and testing, with performance evaluated using accuracy, precision, recall, F1 score, confusion matrix, and dice scores. Results: YOLOv5 achieved a precision of 1.00 at a confidence threshold of 0.910. The ViT system showed an accuracy of 91.66%, with 58 of 64 images predicting correctly with an F1 score of 94.83%. U-Net segmentation after training with annotated images achieved an accuracy of 91% with a dice score of 0.948. Conclusions: The integration of YOLOv5 for detection, ViT for classification, and U-Net for segmentation demonstrates a comprehensive approach to addressing the classification of incisal translucencies. By leveraging the strengths of deep learning models, high accuracy and precision can be achieved in detecting the incisal translucency patterns of anterior teeth. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Verification and Validation of a Vision-Based Landing System for Autonomous VTOL Air Taxis

arXiv

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arXiv 2024年

作者： Bansal, Ayoosh Wang, Duo Yeghiazaryan, Mikael Li, Yangge Tao, Chuyuan Yoon, Hyung-Jin Arora, Prateek Papachristos, Christos Voulgaris, Petros Mitra, Sayan Sha, Lui Hovakimyan, Naira University of Illinois at Urbana-Champaign Illinois Urbana61801 United States University of Nevada RenoNV89557 United States Tennessee Technological University CookevilleTN38505 United States Postdoctoral Research Associate Computer Science 201 North Goodwin Avenue UrbanaIL61801 United States Mechanical Engineering Department 1664 N. Virginia Street RenoNV89557 United States UIUC Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States Graduate Student Electrical and Computer Engineering 306 N. Wright St. UrbanaIL61801 United States Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States Mechanical Engineering Department 1 William L Jones Dr CookevilleTN38505 United States Department of Computer Science & Engineering 1664 N. Virginia Street RenoNV89557 United States Department of Computer Science & Engineering 1664 N. Virginia Street RenoNV89557 United States Mechanical Engineering Department 1664 N. Virginia Street RenoNV89557 United States Electrical and Computer Engineering 306 N. Wright St. UrbanaIL61801 United States Donald B. Gillies Chair in Computer Science Computer Science 201 North Goodwin Avenue UrbanaIL61801 United States Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States

Autonomous air taxis are poised to revolutionize urban mass transportation. A key challenge inhibiting their adoption is ensuring the safety and reliability of the autonomy solutions that will control these vehicles. Validating these solutions on full-scale air taxis in the real world presents complexities, risks, and costs that further convolute the challenge of ensuring safety and reliability of these autonomous vehicles. Verification and Validation (V&V) frameworks play a crucial role in the design and development of highly reliable systems by formally verifying safety properties and validating algorithm behavior across diverse operational scenarios. Advancements in high-fidelity simulators have significantly enhanced their capability to emulate real-world conditions, encouraging their use for validating autonomous air taxi solutions, especially during early development stages. This evolution underscores the growing importance of simulation environments, not only as complementary tools to real-world testing but as essential platforms for evaluating algorithms in a controlled, reproducible, and scalable manner. This work presents a V&V framework for a vision-based landing system for air taxis with vertical take-off and landing (VTOL) capabilities. Specifically, we use Verse, a tool for formal verification, to model and verify the safety of the system by obtaining and analyzing the reachable sets. To conduct this analysis, we utilize a photorealistic simulation environment. The simulation environment, built on Unreal Engine, provides realistic terrain, weather, and sensor characteristics to emulate real-world conditions with high fidelity. To validate the safety analysis results, we conduct extensive scenario-based testing to assess the reachability set and robustness of the landing algorithm in various conditions. This approach showcases the representativeness of high-fidelity simulators, offering an effective means to analyze and refine algorithms before real-wor

关键词： Mass transportation

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Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

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Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

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Monitoring and Analysis of Compensatory Trunk Movements with RGB-D Camera and Wireless System for Rehabilitation of Upper Limbs

Monitoring and Analysis of Compensatory Trunk Movements with...

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IEEE Latin American Robotics Symposium, LARS

作者： Alice T. Garcia André L. S. da Kelbouscas Sibyla A. V. e Silva Leonardo L. Guimarães Vinícius M. de Oliveira Graduate Student in Computer Engineering Graduate Program at the Federal University of Rio Grande (FURG) Rio Grande RS Brazil Associate Professor at Federal University of Rio Grande (FURG) Rio Grande RS Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Assistive Robotics has been shown to be an important tool in the patient rehabilitation process. The rehabilitation of patients with traditional therapeutic approaches has not achieved results, making therapeutic approaches that use robotics gain more and more strength. With this, a series of new ones can be used in order to assist in the rehabilitation of patients with limited restrictions, especially those who have suffered post-stroke trauma. Stroke patients often use the trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, or that results in a reduced effect of rehabilitation training. Detecting trunk displacements can improve the effect of rehabilitation training. Therefore, this study aims to use an RGB-D camera to analyze the movement behavior of the trunk of five volunteers, who do not have any type of restriction, in the execution of four predefined movements. The objective is to verify that, even without any type of restriction, the process of executing movements of the trunk to execute movements. In addition, this study proposes a methodology to perform kinematic tracking of trunk movement compensation through the use of a wireless system. The results obtained with the use of the RGB-D camera were satisfactory and were able to prove the existence of compensatory movements even in patients who do not have any type of restriction.

关键词： Cameras Monitoring Wireless communication Training Tools Stroke (medical condition) Biomedical monitoring

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Applying Association Rules Mining Approach in Skills Competition based on APRIORI Algorithm

Applying Association Rules Mining Approach in Skills Competi...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Mary Ellaine R. Cervantes Daniel D. Dasig Roel C. Traballo Mary Ann B. Taduyo Rudolph Val F. Guarin Eleonora E. Claricia Mengvi P. Gatpandan Denver Jhon R. Calantoc Christine N. Ferrer Lester G. Diampoc Ana Belen Cuyugan Fernando Talion College of Computing and Information Science University of Makati Makati City Philippines Graduate Studies College of Science and Computer Studies De La Salle University- Dasmarinas City of Dasmariñas Graduate School Division Jose Rizal University Mandaluyong City Philippines College of Computer Studies and Engineering Jose Rizal University Mandaluyong City Philippines College of Education Arts and Sciences Jose Rizal University Mandaluyong City Philippines External Linkages and International Affairs Tarlac Agricultural University Tarlac Philippines Student Development Office Community Development Office Jose Rizal University Mandaluyong City Philippines Alfredo M. Velayo College of Accountancy University of Santo Tomas Manila Philippines

In aid of delivering quality Information Technology Education in the Philippines and to upscale the quality of ITE in the country, organizations, and other universities have participated in extra-curricular and academic activities. The study aimed at determining the associated relationships among six hundred competitors and mentors in a skills competition using the APRIORI Algorithm. The CRISP-DM Model and Python programming language were utilized in the study. Accordingly, there were thirteen (13) predicted rules that will allow competing institutions to carefully study before joining the IT Skills Olympics Competition, as such, the rules set can guide each competing school to design a program for their competing teams and select category of the competition to compete in the future. The researchers recommend further empirical analysis using the dataset by employing other machine learning techniques and algorithms.

关键词： Machine learning algorithms Education Humanoid robots Organizations Machine learning Prediction algorithms Data mining

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Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor—the Case Study

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Artificial Intelligence Advances 2019年第2期1卷 38-43页

作者： Shing Tenqchen Yen-Jung Su Keng-Pin Chen ChungHwa Telecom Telecommunication Laboratories(CHTTL)Taiwan Graduate student of Dept.of Electronic&Computer Engineering NTUST

This paper proposes a using Cellular-Based Vehicle Probe(CVP)at road-section(RS)method to detect and setup a model for traffic flow information(info)collection and *** are multiple traffic collection devices including CVP,ETC-Based Vehicle Probe(EVP),Vehicle Detector(VD),and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem,monitor and *** main project has been applied at Tai#2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of *** paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory(LTSM)from recurrent neural network(RNN)*** also provide a model verification and validation methodology with RNN for cross verification of system performance.

关键词： Intelligent Transport Systems(ITS) ETC-Based Vehicle Probe(EVP) Vehicle Detector(VD) Long-Short Time Memory(LTSM) Recurrent Neural Network(RNN)

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